In this work we adapt the locality-sensitive hashing approach of Kitaev et al. (2020) to self-attention in the Transformer, we extended it to cross-attention.

Locality-sensitive hashing has been applied in several problems in bioinformatics to quickly search large sequences by examining the n-grams of the sequences.

Locality-Sensitive Hashing for Long Context Neural Machine Translation. Authors: Frithjof Petrick, Jan Rosendahl, Christian Herold and Hermann Ney.

Nov 24, 2022 � We examine vocabulary selection using Locality. Sensitive Hashing (LSH), and evaluate specifically in the context of Neural Machine Translation.

Locality-Sensitive Hashing for Long Context Neural Machine Translation ... After its introduction the Transformer architecture quickly became the gold standard�...

In this research, we propose a neuralization approach to enhance locality-sensitive hashing by training deep neural networks to serve as hashing functions for�...

This study proposes a locality-sensitive hashing method that can be applied to nearest neighbor search problems for data sets containing both numerical and�...

Missing: Translation. | Show results with:Translation.

Jul 22, 2024 � , 2020], locally-sensitive hashing (LSH) is used to identify the most significant terms in the summation implied by the dot product QKT�...

Sep 25, 2023 � Locality-sensitive hashing for � long context neural machine translation. In Proceed- ings of the 19th International Conference on Spoken.